标题： 利用排行榜进行恶意模型的大规模分发 显示英文标题

标题： Exploiting Leaderboards for Large-Scale Distribution of Malicious Models

摘要： 虽然对机器学习模型的中毒攻击已经得到了广泛研究，但对手如何大规模分发被污染的模型的机制仍然 largely 未被探索。 在本文中，我们揭示了模型排行榜——用于模型发现和评估的排名平台——如何可以成为对手进行隐蔽的大规模分发被污染模型的强大渠道。 我们提出了 TrojanClimb，一个通用框架，它能够在保持竞争性排行榜表现的同时注入恶意行为。 我们在四种不同的模态中展示了其有效性：文本嵌入、文本生成、文本到语音和文本到图像，表明对手可以在嵌入任意有害功能（从后门到偏见注入）的同时成功获得高排行榜排名。 我们的发现揭示了机器学习生态系统中的一个重大漏洞，突显了重新设计排行榜评估机制以检测和过滤恶意（例如，被污染的）模型的紧迫性，同时暴露了机器学习社区在采用未经验证来源的模型时的更广泛的安全影响。 显示英文摘要

摘要： While poisoning attacks on machine learning models have been extensively studied, the mechanisms by which adversaries can distribute poisoned models at scale remain largely unexplored. In this paper, we shed light on how model leaderboards -- ranked platforms for model discovery and evaluation -- can serve as a powerful channel for adversaries for stealthy large-scale distribution of poisoned models. We present TrojanClimb, a general framework that enables injection of malicious behaviors while maintaining competitive leaderboard performance. We demonstrate its effectiveness across four diverse modalities: text-embedding, text-generation, text-to-speech and text-to-image, showing that adversaries can successfully achieve high leaderboard rankings while embedding arbitrary harmful functionalities, from backdoors to bias injection. Our findings reveal a significant vulnerability in the machine learning ecosystem, highlighting the urgent need to redesign leaderboard evaluation mechanisms to detect and filter malicious (e.g., poisoned) models, while exposing broader security implications for the machine learning community regarding the risks of adopting models from unverified sources.